Feature Extraction: The Aesthetics and Politics of Algorithms

Link to research

The Feature Extraction ASSEMBLY included a series of talks, demos, workshops, and discussion groups that brought together artists and researchers engaged with machine-learning. Under the banner of “artificial intelligence,” machine learning has become central to interlocking domains of political-economic control, including: predictive policing, financialization, ad-tech, social media, and logistics. Unlike the proprietary algorithms used in these applications, artistic uses of machine learning allow people to experience and engage with algorithms directly. In this series, we paired artistic uses of machine learning with scholarly research to explore the social repercussions of algorithmic governance under algorithmic capitalism.

Organized by Blaine O’Neill and Ulysses Pascal | Blaine.d.oneill[at]gmail.com | upascal[at]gmail.com | http://feature-extraction.ai/

Documentation / Resources

Link to researchWatch Pitch Presentation

Group members

Blaine O’Neill, Ulysses Pascal, Bela Abolfathi, Carla Orendorff, Cecile Thompson-Hannant, Chase Niesner, Chris Tyler, Dean Lenoir, Harry Hvdson, Leah Wulfman, Leming Zhong, Muxi Zhuo, Parag Mital, Riley O’Neill, Sara Drake, Selwa Sweidan, Soffi Stiassni

Guest speakers

Nora Khan
Peli Grietzer
Harry Hvdson from Stop LAPD spying
Stalgia Grigg


The Feature Extraction ASSEMBLY group used their microgrant ($225) to pay honorariums for guest speakers.

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